Reinforcement learning (RL) is hot! This branch of machine learning powers AlphaGo and Deepmind's Atari AI. It allows programmers to create software agents that learn to take optimal actions to maximize reward, through trying out different strategies in a given environment.

This course will take you through all the core concepts in Reinforcement Learning, transforming a theoretical subject into tangible Python coding exercises with the help of OpenAI Gym. The videos will first guide you through the gym environment, solving the CartPole-v0 toy robotics problem, before moving on to coding up and solving a multi-armed bandit problem in Python. As the course ramps up, it shows you how to use dynamic programming and TensorFlow-based neural networks to solve GridWorld, another OpenAI Gym challenge. Lastly, we take the Blackjack challenge and deploy model free algorithms that leverage Monte Carlo methods and Temporal Difference (TD, more specifically SARSA) techniques.

The scope of Reinforcement Learning applications outside toy examples is immense. Reinforcement Learning can optimize agricultural yield in IoT powered greenhouses, and reduce power consumption in data centers. It's grown in demand to the point where its applications range from controlling robots to extracting insights from images and natural language data. By the end of this course, you will not only be able to solve these problems but will also be able to use Reinforcement Learning as a problem-solving strategy and use different algorithms to solve these problems.

Style and Approach

Reinforcement Learning is about two things: framing the action, state, and reward correctly, and optimizing the policy that the software agent will use to approach the problem.

This action-packed course is grounded in Python code that you can follow along with and takes you through all the main pillars of Reinforcement Learning. Leveraging Python, TensorFlow, NumPy, and OpenAI Gym, you get to try things out and understand a powerful technology through practical examples.

Features

Learn how to solve Reinforcement Learning problems with a variety of strategies.

Authors

Rudy Lai

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and Cloud computing.

Over the past few years, they have worked with some of the World's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways.The company lives by its motto: Data -> Intelligence -> Action.

Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails for prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generates content.

Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first-hand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with HighDimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye.

In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which you can learn about reinforcement learning and supervised learning topics in depth and in a commercial setting.

Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize.

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